Current
Quantum Algorithm Engineer
Kipu Quantum
Quantum algorithms and scientific machine learning.
Antonio Ferrer-Sánchez
Physicist · Quantum algorithms · Scientific machine learning
Updated 1 November 2026
Experience
Current
PhD student
Universitat de València · IDAL
Deep learning methods for physical systems governed by differential equations.
February–March 2026
Senior Research Technician
Universitat de València · Department of Electronic Engineering
Temporary research contract at ETSE-UV.
2022–2025
Predoctoral researcher
Universitat de València
Research on deep learning methods for solving the equations that govern physical systems and modelling their evolution.
2020–2022
Artificial Intelligence Software Developer
Capgemini
Applied machine learning, anomaly detection and computer vision.
Education
2021
Master’s Degree in Data Science
Universitat de València
2019
Master’s Degree in Advanced Physics
Universitat de València
2018
Degree in Physics
Universitat de València
Teaching
Practical teaching in the Data Science degree at Universitat de València.
September–December 2024
Big Data
Universitat de València
January–June 2024
Natural Language Processing
Universitat de València
September–December 2023
Big Data
Universitat de València
Grants
2022–2024
ValgrAI grant for predoctoral researchers
ValgrAI · Universitat de València
Predoctoral research grant from the Valencian Graduate School and Research Network of Artificial Intelligence, held in the Department of Electronic Engineering.
Patents
2023
Patent application 24163901.2
Kipu Quantum
Co-inventor; Kipu Quantum holds the rights. Registered in Germany on 11 August 2023, with co-inventors from Universitat de València and Kipu Quantum.
Selected publications & preprints
Kerr Quasinormal Modes without Variable Separation: A Two-Dimensional Hyperboloidal Teukolsky Solver with Physics-Informed Neural Networks
Antonio Ferrer-Sánchez, Daniela D. Doneva, José D. Martín-Guerrero, Stoytcho S. Yazadjiev, Roberto Ruiz de Austri-Bazan, Yolanda Vives-Gilabert, José A. Font. arXiv:2608.19774, 2026. Preprint.
Computes the oscillation frequencies of rotating black holes with physics-informed neural networks. A two-dimensional solver preserves the coupled radial-angular structure without separating variables.
https://arxiv.org/abs/2608.19774
Physics-Informed Neural Networks for Maximizing Quantum Fisher Information in Time-Dependent Many-Body Systems
Antonio Ferrer-Sánchez, Yolanda Vives-Gilabert, Yue Ban, Xi Chen, José D. Martín-Guerrero. arXiv:2604.18506, 2026. Preprint.
Uses physics-informed neural networks to design control protocols that increase quantum Fisher information in driven spin systems. The work connects quantum dynamics and control with improved sensitivity for parameter estimation.
https://arxiv.org/abs/2604.18506
Addressing the gravitational collapse of a massless scalar field with physics-informed neural networks
Antonio Ferrer-Sánchez, Nino Villanueva-Espinosa, Carlos Hernani-Morales, Roberto Ruiz de Austri-Bazan, José A Font, José David Martín-Guerrero, Matthew W Choptuik. Machine Learning: Science and Technology, 2026. Journal article.
Compares neural architectures for scalar-field dispersion and gravitational collapse, including the threshold of black-hole formation. The study examines how network design and adaptive sampling affect accuracy and stability.
https://doi.org/10.1088/2632-2153/ae5459
Trackformers: in search of transformer-based particle tracking for the high-luminosity LHC era
Sascha Caron, Nadezhda Dobreva, Antonio Ferrer Sánchez, José D. Martín-Guerrero, Uraz Odyurt, Roberto Ruiz de Austri Bazan, Zef Wolffs, Yue Zhao. The European Physical Journal C, 2025. Journal article.
Compares transformer-based methods for associating detector hits with particle tracks in simulations for the high-luminosity LHC. It examines reconstruction accuracy and computational cost for sequential and one-shot approaches.
https://doi.org/10.1140/epjc/s10052-025-14156-3
Gradient-annihilated PINNs for solving Riemann problems: Application to relativistic hydrodynamics
Antonio Ferrer-Sánchez, José D. Martín-Guerrero, Roberto Ruiz de Austri-Bazan, Alejandro Torres-Forné, José A. Font. Computer Methods in Applied Mechanics and Engineering, 2024. Journal article.
Introduces a weighted physics loss that reduces the influence of steep gradients when learning discontinuous solutions. Tests on relativistic Riemann problems assess how well the method captures shocks and other fluid discontinuities.
https://doi.org/10.1016/j.cma.2024.116906
Physics-informed neural networks for an optimal counterdiabatic quantum computation
Antonio Ferrer-Sánchez, Carlos Flores-Garrigos, Carlos Hernani-Morales, José J Orquín-Marqués, Narendra N Hegade, Alejandro Gomez Cadavid, Iraitz Montalban, Enrique Solano, Yolanda Vives-Gilabert, José D Martín-Guerrero. Machine Learning: Science and Technology, 2024. Journal article.
Learns counterdiabatic controls from physical constraints to suppress unwanted transitions during quantum computation. A two-qubit molecular example demonstrates the joint design of control schedules and adiabatic gauge potentials.
https://doi.org/10.1088/2632-2153/ad450f
Prediction of the risk of cancer and the grade of dysplasia in leukoplakia lesions using deep learning
Antonio Ferrer-Sánchez, Jose Bagan, Joan Vila-Francés, Rafael Magdalena-Benedito, Leticia Bagan-Debon. Oral Oncology, 2022. Journal article.
Investigates deep learning to estimate malignant transformation and high-risk dysplasia from photographs of oral leukoplakia. The pipeline combines lesion segmentation, classification and heatmaps to help interpret its predictions.
https://doi.org/10.1016/j.oraloncology.2022.105967
Scientific activity
2026
International Quantum Matter Conference & Expo (QUANTUMatter 2026)
Maximizing quantum Fisher information with physics-informed neural networks. Conference contribution · Barcelona, Spain.
2024
Winter School on Quantum Machine Learning
Physics-informed neural networks for an optimal counterdiabatic quantum computation. Conference contribution · Trento, Italy.
2024
Quantum Control and Computing Conference
Physics-informed neural networks for an optimal counterdiabatic quantum computation. Oral presentation · Berlin, Germany.
2024
European AI for Fundamental Physics Conference
Gradient-annihilated PINNs for solving Riemann problems. Poster · Amsterdam, Netherlands.
2023
Granada Seminar
Gradient-annihilated PINNs for solving Riemann problems. Oral presentation · Online.
2023
Brown University webinar
Gradient-annihilated PINNs for solving Riemann problems. Oral presentation · Online.
Public software
PINNs for counterdiabatic driving
Jupyter tutorials, trained models, data, and code for counterdiabatic quantum computation using physics-informed neural networks.
https://github.com/Netzuel/pinns-for-counterdiabatic-driving
ModPINN gravitational collapse
Physics-informed neural networks for spherical scalar-field gravitational collapse, with training code, pretrained models, reference data and Jupyter tutorials.
https://github.com/Netzuel/modpinn-gravitational-collapse
GA-PINNs
Gradient-annihilated physics-informed neural networks for Riemann problems in relativistic hydrodynamics.
https://github.com/Netzuel/GA_PINNs_Repository
Adversarial Manuscript Review
A bounded manuscript review and revision workflow using native agents in Codex and Claude Code. A research aid, not a substitute for scientific judgment or peer review.
https://github.com/Netzuel/adversarial-manuscript-review